OpenRaiser/Intern-Atlas huggingface dataset:
Vehicle Detection and Tracking Techniques: A Concise Review
Venue: arXiv cs.CV 2014
Year: 2014

Vehicle detection and tracking applications play an important role for civilian and military applications such as in highway traffic surveillance control, management and urban traffic planning. Vehicle detection process on road are used for vehicle tracking, counts, average speed of each individual vehicle, traffic analysis and vehicle categorizing objectives and may be implemented under different environments changes. In this review, we present a concise overview of image processing methods and analysis tools which used in building these previous mentioned applications that involved developing traffic surveillance systems. More precisely and in contrast with other reviews, we classified the processing methods under three categories for more clarification to explain the traffic systems.
Constellation Design for Multi-color Visible Light Communications
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we propose a novel high dimensional constellation design scheme for visible light communication (VLC) systems employing red/green/blue light emitting diodes (RGB LEDs). It is in fact a generalized color shift keying (CSK) scheme which does not suffer efficiency loss due to a constrained sum intensity for all constellation symbols. Crucial lighting requirements are included as optimization constraints. To control non-linear distortion, the optical peak-to-average-power ratio (PAPR) of LEDs is individually constrained. Fixing the average optical power, our scheme is able to achieve much lower bit-error rate (BER) than conventional schems especially when illumination color is more "unbalanced". When cross-talks exist among the multiple optical channels, we apply a singular value decomposition (SVD)-based pre-equalizer and redesign the constellations, and such scheme is shown to outperform post-equalized schemes based on zero-forcing or linear minimum-mean-squared-error (LMMSE) principles. To further reduce system BER, a binary switching algorithm (BSA) is employed the first time for labeling high dimensional constellation. We thus obtains the optimal bits-to-symbols mapping.
Fuchsian codes with arbitrarily high code rate
Venue: arXiv cs.IT 2014
Year: 2014

Recently, so-called Fuchsian codes have been proposed in [I. Blanco-Chacón et al., "Nonuniform Fuchsian codes for noisy channels", J. of the Franklin Institute 2014] for communication over channels subject to additive white Gaussian noise (AWGN). The two main advantages of Fuchsian codes are their ability to compress information, i.e., high code rate, and their logarithmic decoding complexity. In this paper, we improve the first property further by constructing Fuchsian codes with arbitrarily high code rates while maintaining logarithmic decoding complexity. Namely, in the case of Fuchsian groups derived from quaternion algebras over totally real fields we obtain a code rate that is proportional to the degree of the base field. In particular, we consider arithmetic Fuchsian groups of signature (1;e) to construct explicit codes having code rate six, meaning that we can transmit six independent integers during one channel use.
Building Block Components to Control a Data Rate in the Apache Hadoop Compute Platform
Venue: arXiv cs.DC 2014
Year: 2014

Resource management is one of the most indispensable components of cluster-level infrastructure layers. Users of such systems should be able to specify their job requirements as a configuration parameter (CPU, RAM, disk I/O, network I/O) and have the scheduler translate those into an appropriate reservation and allocation of resources. YARN is an emerging resource management in the Hadoop ecosystem, which supports only RAM and CPU reservation at present. In this paper, we propose a solution that takes into account the operation of the Hadoop Distributed File System to control the data rate of applications in the framework of a Hadoop compute platform. We utilize the property that a data pipe between a container and a DataNode consists of a disk I/O subpipe and a TCP/IP subpipe. We have implemented building block software components to control the data rate of data pipes between containers and DataNodes and provide a proof-of-concept with measurement results.
A QoE-Based Scheduling Algorithm for UGS Service Class in WiMAX Network
Venue: arXiv cs.NI 2014
Year: 2014

To satisfy the increasing demand for multimedia services in broadband Internet networks, the WiMAX (Worldwide Interoperability for Microwave Acces) technology has emerged as an alternative to the wired broadband access solutions. It provides an Internet connection to broadband coverage area of several kilometers in radius by ensuring a satisfactory quality of service (QoS), it's an adequate response to some rural or inaccessible areas. Unlike DSL (Digital Subscriber Line) or other wired technology, WiMAX uses radio waves and can provide point-to-multipoint (PMP) and point-to-point (P2P) modes. In parallel, it's observed that in the opposite of the traditional quality evaluation approaches, nowadays, current researches focus on the user perceived quality, the existing scheduling algorithms take into account the QoS and many other parameters, but not the Quality of Experience (QoE). In this paper, we present a QoE-based scheduling solution in WiMAX network in order to make the scheduling of the UGS connections based on the use of QoE metrics. Indeed, the proposed solution allows controlling the packet transmission rate so as to match with the minimum subjective rate requirements of each user. Simulation results show that by applying various levels of mean opinion score (MOS) the QoE provided to the users is improved in term of throughput, jitter, packet loss rate and delay.
A Novel Visual Word Co-occurrence Model for Person Re-identification
Venue: arXiv cs.CV 2014
Year: 2014

Person re-identification aims to maintain the identity of an individual in diverse locations through different non-overlapping camera views. The problem is fundamentally challenging due to appearance variations resulting from differing poses, illumination and configurations of camera views. To deal with these difficulties, we propose a novel visual word co-occurrence model. We first map each pixel of an image to a visual word using a codebook, which is learned in an unsupervised manner. The appearance transformation between camera views is encoded by a co-occurrence matrix of visual word joint distributions in probe and gallery images. Our appearance model naturally accounts for spatial similarities and variations caused by pose, illumination & configuration change across camera views. Linear SVMs are then trained as classifiers using these co-occurrence descriptors. On the VIPeR and CUHK Campus benchmark datasets, our method achieves 83.86% and 85.49% at rank-15 on the Cumulative Match Characteristic (CMC) curves, and beats the state-of-the-art results by 10.44% and 22.27%.
Efficient LTE Access with Collision Resolution for Massive M2M Communications
Venue: arXiv cs.IT 2014
Year: 2014

LTE random access procedure performs satisfactorily in case of asynchronous, uncorrelated traffic arrivals. However, when the arrivals are correlated and arrive synchronously, the performance of the random access channel (RACH) is drastically reduced, causing a large number of devices to experience outage. In this work we propose a LTE RACH scheme tailored for delay-sensitive M2M services with synchronous traffic arrivals. The key idea is, upon detection of a RACH overload, to apply a collision resolution algorithm based on splitting trees. The solution is implemented on top of the existing LTE RACH mechanism, requiring only minor modifications of the protocol operation and not incurring any changes to the physical layer. The results are very promising, outperforming the related solutions by a wide margin. As an illustration, the proposed scheme can resolve 30k devices with an average of 5 preamble transmissions and delay of 1.2 seconds, under a realistic probability of transmissions error both in the downlink and in the uplink.
Detecting Figures and Part Labels in Patents: Competition-Based Development of Image Processing Algorithms
Venue: arXiv cs.CV 2014
Year: 2014

We report the findings of a month-long online competition in which participants developed algorithms for augmenting the digital version of patent documents published by the United States Patent and Trademark Office (USPTO). The goal was to detect figures and part labels in U.S. patent drawing pages. The challenge drew 232 teams of two, of which 70 teams (30%) submitted solutions. Collectively, teams submitted 1,797 solutions that were compiled on the competition servers. Participants reported spending an average of 63 hours developing their solutions, resulting in a total of 5,591 hours of development time. A manually labeled dataset of 306 patents was used for training, online system tests, and evaluation. The design and performance of the top-5 systems are presented, along with a system developed after the competition which illustrates that winning teams produced near state-of-the-art results under strict time and computation constraints. For the 1st place system, the harmonic mean of recall and precision (f-measure) was 88.57% for figure region detection, 78.81% for figure regions with correctly recognized figure titles, and 70.98% for part label detection and character recognition. Data and software from the competition are available through the online UCI Machine Learning repository to inspire follow-on work by the image processing community.
A Framework for On-Line Devanagari Handwritten Character Recognition
Venue: arXiv cs.CV 2014
Year: 2014

The main challenge in on-line handwritten character recognition in Indian lan- guage is the large size of the character set, larger similarity between different characters in the script and the huge variation in writing style. In this paper we propose a framework for on-line handwitten script recognition taking cues from speech signal processing literature. The framework is based on identify- ing strokes, which in turn lead to recognition of handwritten on-line characters rather that the conventional character identification. Though the framework is described for Devanagari script, the framework is general and can be applied to any language. The proposed platform consists of pre-processing, feature extraction, recog- nition and post processing like the conventional character recognition but ap- plied to strokes. The on-line Devanagari character recognition reduces to one of recognizing one of 69 primitives and recognition of a character is performed by recognizing a sequence of such primitives. We further show the impact of noise removal on on-line raw data which is usually noisy. The use of Fuzzy Direc- tional Features to enhance the accuracy of stroke recognition is also described. The recognition results are compared with commonly used directional features in literature using several classifiers.
A World of Views: A World of Interacting Post-human Intelligences
Venue: arXiv cs.CY 2014
Year: 2014

What would a human hundreds or thousands times more intelligent than the brightest human ever born be like? We must admit we can hardly guess. A human being of such intelligence will be so radically different from us that it can hardly, if at all, be recognized as human. If we had to go back along the evolutionary tree to identify a creature 1000 times less intelligent than the average contemporary human, we will have to go really far back. Would it be a kind of a lizard? An insect perhaps? Considering this, how can we possibly aspire to have a grasp of something a thousand times more intelligent than us? When it comes to intelligence, even the very attempt to quantify it is highly misleading. Now if we attend to a seemingly adjacent question, what would a machine with such capacity for intelligence be like? Just coming up with an approximate metaphor requires a huge stretch of the imagination, meaning that almost anything goes... What would a society of such super intelligent agents, be they human, machines or an amalgam of both, be like? Well, here we are transported into the realm of pure speculation. Technological Singularity is referred to as the event of artificial intelligence surpassing the intelligence of humans and shortly after augmenting itself far beyond that. It is no wonder that the mathematical concept of singularity has become the symbol of an event so disruptive and so far reaching that it is impossible to conceptually or even metaphorically grasp, much less to predict.
Optimal topology of multi-agent systems with two leaders: a zero-sum game perspective
Venue: arXiv cs.SY 2014
Year: 2014

It is typical to assume that there is no conflict of interest among leaders. Under such assumption, it is known that, for a multi-agent system with two leaders, if the followers' interaction subgraph is undirected and connected, then followers will converge to a convex combination of two leaders' states with linear consensus protocol. In this paper, we introduce the conflict between leaders: by choosing k followers to connect with, every leader attempts all followers converge to himself closer than that of the other. By using graph theory and matrix theory, we formulate this conflict as a standard two-player zero-sum game and give some properties about it. It is noteworthy that the interaction graph here is generated from the conflict between leaders. Interestingly, we find that to find the optimal topology of the system is equivalent to solve a Nash equilibrium. Especially for the case of choosing one connected follower, the necessary and sufficient condition for an interaction graph to be the optimal one is given. Moreover, if followers' interaction graph is a circulant graph or a graph with a center node, then the system's optimal topology is obtained. Simulation examples are provided to validate the effectiveness of the theoretical results.
The probatilistic Quantifier Fuzzification Mechanism FA: A theoretical analysis
Venue: arXiv cs.AI 2014
Year: 2014

The main goal of this work is to analyze the behaviour of the FA quantifier fuzzification mechanism. As we prove in the paper, this model has a very solid theorethical behaviour, superior to most of the models defined in the literature. Moreover, we show that the underlying probabilistic interpretation has very interesting consequences.
Iris Biometric System using a hybrid approach
Venue: arXiv cs.CV 2014
Year: 2014

Iris Recognition Systems are ocular- based biometric devices used primarily for security reasons. The complexity and the randomness of the Iris, amongst various other factors, ensure that this biometric system is inarguably an exact and reliable method of identification. The algorithm is responsible for automatic localization and segmentation of boundaries using circular Hough Transform, noise reductions, image enhancement and feature extraction across numerous distinct images present in the database. This paper delves into the various kinds of techniques required to approximate the pupillary and limbic boundaries of the enrolled iris image, captured using a suitable image acquisition device and perform feature extraction on the normalized iris image with the help of Haar Wavelets to encode the input data into a binary string format. These techniques were validated using images from the CASIA database, and various other procedures were also tried and tested.
Deterministic Extractors for Additive Sources
Venue: arXiv cs.CC 2014
Year: 2014

We propose a new model of a weakly random source that admits randomness extraction. Our model of additive sources includes such natural sources as uniform distributions on arithmetic progressions (APs), generalized arithmetic progressions (GAPs), and Bohr sets, each of which generalizes affine sources. We give an explicit extractor for additive sources with linear min-entropy over both $\mathbb{Z}_p$ and $\mathbb{Z}_p^n$, for large prime $p$, although our results over $\mathbb{Z}_p^n$ require that the source further satisfy a list-decodability condition. As a corollary, we obtain explicit extractors for APs, GAPs, and Bohr sources with linear min-entropy, although again our results over $\mathbb{Z}_p^n$ require the list-decodability condition. We further explore special cases of additive sources. We improve previous constructions of line sources (affine sources of dimension 1), requiring a field of size linear in $n$, rather than $Ω(n^2)$ by Gabizon and Raz. This beats the non-explicit bound of $Θ(n \log n)$ obtained by the probabilistic method. We then generalize this result to APs and GAPs.
Capacity Analysis of Decoupled Downlink and Uplink Access in 5G Heterogeneous Systems
Venue: arXiv cs.NI 2014
Year: 2014

Our traditional notion of a cell is changing dramatically given the increasing degree of heterogeneity in 4G and emerging 5G systems. Rather than belonging to a specific cell, a device would choose the most suitable connection from the plethora of connections available. In such a setting, given the transmission powers differ significantly between downlink (DL) and uplink (UL), a wireless device that sees multiple Base Stations (BSs) may access the infrastructure in a way that it receives the downlink (DL) traffic from one BS and sends uplink (UL) traffic through another BS. This situation is referred to as Downlink and Uplink Decoupling (DUDe). In this paper, the capacity and throughput gains brought by decoupling are rigorously derived using stochastic geometry. Theoretical findings are then corroborated by means of simulation results. A further constituent of this paper is the verification of the theoretically derived results by means of a real-world system simulation platform. Despite theoretical assumptions differing from the very complete system simulator, the trends in the association probabilities and capacity gains are similar. Based on the promising results, we then outline architectural changes needed to facilitate the decoupling of DL and UL.
On Sparse Vector Recovery Performance in Structurally Orthogonal Matrices via LASSO
Venue: arXiv cs.IT 2014
Year: 2014

In this paper, we consider a compressed sensing problem of reconstructing a sparse signal from an undersampled set of noisy linear measurements. The regularized least squares or least absolute shrinkage and selection operator (LASSO) formulation is used for signal estimation. The measurement matrix is assumed to be constructed by concatenating several randomly orthogonal bases, referred to as structurally orthogonal matrices. Such measurement matrix is highly relevant to large-scale compressive sensing applications because it facilitates fast computation and also supports parallel processing. Using the replica method from statistical physics, we derive the mean-squared-error (MSE) formula of reconstruction over the structurally orthogonal matrix in the large-system regime. Extensive numerical experiments are provided to verify the analytical result. We then use the analytical result to study the MSE behaviors of LASSO over the structurally orthogonal matrix, with a particular focus on performance comparisons to matrices with independent and identically distributed (i.i.d.) Gaussian entries. We demonstrate that the structurally orthogonal matrices are at least as well performed as their i.i.d. Gaussian counterparts, and therefore the use of structurally orthogonal matrices is highly motivated in practical applications.
Fast Compressive Phase Retrieval from Fourier Measurements
Venue: arXiv cs.IT 2014
Year: 2014

This paper considers the problem of recovering a $k$-sparse, $N$-dimensional complex signal from Fourier magnitude measurements. It proposes a Fourier optics setup such that signal recovery up to a global phase factor is possible with very high probability whenever $M \gtrsim 4k\log_2(N/k)$ random Fourier intensity measurements are available. The proposed algorithm is comprised of two stages: An algebraic phase retrieval stage and a compressive sensing step subsequent to it. Simulation results are provided to demonstrate the applicability of the algorithm for noiseless and noisy scenarios.
Feature Selection through Minimization of the VC dimension
Venue: arXiv cs.LG 2014
Year: 2014

Feature selection involes identifying the most relevant subset of input features, with a view to improving generalization of predictive models by reducing overfitting. Directly searching for the most relevant combination of attributes is NP-hard. Variable selection is of critical importance in many applications, such as micro-array data analysis, where selecting a small number of discriminative features is crucial to developing useful models of disease mechanisms, as well as for prioritizing targets for drug discovery. The recently proposed Minimal Complexity Machine (MCM) provides a way to learn a hyperplane classifier by minimizing an exact (\boldmath{$Θ$}) bound on its VC dimension. It is well known that a lower VC dimension contributes to good generalization. For a linear hyperplane classifier in the input space, the VC dimension is upper bounded by the number of features; hence, a linear classifier with a small VC dimension is parsimonious in the set of features it employs. In this paper, we use the linear MCM to learn a classifier in which a large number of weights are zero; features with non-zero weights are the ones that are chosen. Selected features are used to learn a kernel SVM classifier. On a number of benchmark datasets, the features chosen by the linear MCM yield comparable or better test set accuracy than when methods such as ReliefF and FCBF are used for the task. The linear MCM typically chooses one-tenth the number of attributes chosen by the other methods; on some very high dimensional datasets, the MCM chooses about $0.6\%$ of the features; in comparison, ReliefF and FCBF choose 70 to 140 times more features, thus demonstrating that minimizing the VC dimension may provide a new, and very effective route for feature selection and for learning sparse representations.
Arrival Modeling and Error Analysis for Molecular Communication via Diffusion with Drift
Venue: arXiv cs.ET 2014
Year: 2014

The arrival of molecules in molecular communication via diffusion (MCvD) is a counting process, exhibiting by its nature binomial distribution. Even if the binomial process describes well the arrival of molecules, when considering consecutively sent symbols, the process struggles to work with the binomial cumulative distribution function (CDF). Therefore, in the literature, Poisson and Gaussian approximations of the binomial distribution are used. In this paper, we analyze these two approximations of the binomial model of the arrival process in MCvD with drift. Considering the distance, drift velocity, and the number of emitted molecules, we investigate the regions in which either Poisson or Gaussian model is better in terms of root mean squared error (RMSE) of the CDFs; we confirm the boundaries of the region via numerical simulations. Moreover, we derive the error probabilities for continuous communication and analyze which model approximates it more accurately.